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Every AI workflow needs someone officially responsible for catching it when it's wrong.
That sounds obvious, but that human oversight doesn't show up often enough in practice.
A March trial revealed that a government spreadsheet flagged an HVAC grant as a DEI initiative. The wholesale prompt for every line item was, "does this relate at all to DEI?" The model said yes to HVAC — improved climate control would support access to diverse audiences. A $349,000 grant to preserve museum artifacts was canceled and only partially restored later.
No rubric, no domain expertise, and no definition of DEI provided. The prompt was the policy.
The people who could have caught it — NEH's own career staff — had already reviewed the grants and marked them unproblematic, but their assessments were overridden anyway. The expertise was there, and it just wasn't consulted.
That's what everyone should focus on here. Not the chatbot or the politics. It's the unfounded decision to stop asking the people who knew best.
Variations of this pitfall exist in hiring pipelines, content moderation systems, customer service queues, and vendor evaluations across nearly every industry. The question isn't whether AI should be involved in workflows. We should be asking the harder one: What do human staff need to be entrusted with — and what happens when the model is wrong?
If you can't answer that before rolling out AI, the spreadsheet is already running the show.
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Hot take: The more artificial the technology gets, the more human they make the branding become.
Look at Granola, Wispr Flow and of course Anthropic, and dozens of newer AI startups. Serif typography is everywhere.
For years, tech chased clean sans serifs, gradients, efficiency, and neutrality. Now the trend is warmth, personality, and a more human feel.
Maybe that's the irony of AI.
The branding is getting more human because the technology is getting less human. Feels like a natural correction.
I joined the Config Makeathon and built BriefOS — a client brief operating system for designers, studios, and freelancers.
The problem I wanted to solve is something almost every designer deals with:
Client projects rarely start clean.
They usually start with scattered notes, vague goals, random references, unclear positioning, mixed opinions, and no approved direction. That chaos creates slow starts, endless revisions, weak strategy, and confusion between the designer and the client.
So I built BriefOS to turn that mess into a clear, structured website blueprint.
The idea:
Client chaos → AI signal extraction → website blueprint → visual direction → wireframe → client approval
Inside BriefOS, a designer can collect messy client input, organize it into useful categories, extract important signals, build a sitemap, generate copy directions, choose visual routes, adjust wireframes, and send decisions into a client approval room.
I wanted the product to feel less like a generic AI tool and more like a real project command center.
What I built:
Command Center to track the full project workflow
Intake Wall for messy client notes, links, screenshots, and references
Signal Extractor to identify offer, audience, CTA, tone, gaps, and contradictions
Blueprint Builder for sitemap, homepage structure, copy, and conversion path
Visual Lab for mood, palette, typography, layout direction, and style routes
Wireframe Studio for reviewing and refining the homepage structure
Approval Room for client decisions, comments, approvals, and revision requests
I also focused on making the experience interactive instead of just “AI magically does everything.”
The user can tag notes, approve or reject signals, choose direction, reorder sections, compare options, adjust wireframes, and approve final decisions.
Built using Figma Make as part of the Config Makeathon.
The goal was simple:
Help designers move from confusion to clarity faster — while keeping strategy, copy, design, and client approval connected in one workflow.
Still more I’d love to improve:
deeper real data inputs
stronger drag-and-drop behavior
better client presentation mode
more polished animations
live export options
more visual direction templates
But for this version, I’m happy with the core concept: a useful AI workflow product built around a real design problem.
preview link:https://www.figma.com/make/LhthPqSGpwgLBKVoJdQLHG/Begin-Project?fullscreen=1&t=aPQZNZHYQiD53qn0-1&code-node-id=0-9
#ConfigMakeathon @figma #FigmaMake #ProductDesign #UIDesign #AIDesign #WebDesign #DesignTools #Contra
This is actually a really useful idea. Turning messy client briefs into a clear workflow solves a real problem.
How AI Is Changing Full-Stack Development
AI is no longer just a tool for automation. It is becoming a real partner in software development.
As a full-stack developer, I use AI to speed up planning, improve code quality, generate ideas, debug faster, and build smarter user experiences. But the key is not blindly copying AI-generated code. The real value comes from understanding the logic, reviewing the output, and using AI to support better engineering decisions.
Modern products are moving toward AI-powered features like chatbots, recommendation systems, smart search, workflow automation, and personalized dashboards. Developers who understand both full-stack engineering and AI integration can build faster, more scalable, and more useful products.
AI will not replace strong developers, but developers who use AI properly will move much faster than those who do not.
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